Free PDF Quiz 2026 Microsoft DP-100: Updated Designing and Implementing a Data Science Solution on Azure Valid Dumps Free

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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Design and prepare a machine learning solution20-25%- Manage compute resources
  • 1. Select environments
  • 2. Create and configure compute targets
  • 3. Attach and monitor compute
- Manage Azure Machine Learning workspace
  • 1. Create and configure workspace
  • 2. Set up Git integration
  • 3. Use developer tools and CLI
  • 4. Work with registries
- Design a machine learning solution
  • 1. Determine dataset structure and format
  • 2. Select development approach
  • 3. Define compute specifications for workloads
  • 4. Plan model deployment requirements
- Manage data assets
  • 1. Select storage services
  • 2. Create and maintain data assets
  • 3. Register and manage datastores
Topic 2: Train and deploy models25-30%- Monitor and maintain models
  • 1. Update and retrain models
  • 2. Monitor performance and data drift
  • 3. Implement MLOps practices
- Train models
  • 1. Use HyperDrive for hyperparameter tuning
  • 2. Apply responsible AI principles
  • 3. Run training scripts
  • 4. Configure jobs and environments
- Deploy models
  • 1. Secure endpoints and manage access
  • 2. Configure compute and scaling
  • 3. Deploy to batch endpoints
  • 4. Deploy to online endpoints
- Manage models
  • 1. Interpret models and explain predictions
  • 2. Register and version models
  • 3. Package and validate models
Topic 3: Optimize language models for AI applications25-30%- Implement generative AI solutions
  • 1. Apply prompt engineering
  • 2. Build prompt flows
  • 3. Use Azure AI Foundry
- Evaluate and improve models
  • 1. Optimize for accuracy and safety
  • 2. Test and evaluate responses
  • 3. Apply responsible generative AI
- Optimize with Retrieval Augmented Generation
  • 1. Create vector stores and indexes
  • 2. Prepare and process data
  • 3. Configure Azure AI Search
Topic 4: Explore data and run experiments20-25%- Implement pipelines
  • 1. Pass data between steps
  • 2. Build reusable components
  • 3. Schedule and monitor pipelines
  • 4. Create and publish pipelines
- Run experiments
  • 1. Track runs with MLflow
  • 2. Define parameters and configurations
  • 3. Configure experiment runs
  • 4. Use automated machine learning
- Explore and visualize data
  • 1. Profile and validate data
  • 2. Detect anomalies and outliers
  • 3. Identify features and relationships

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q416-Q421):

NEW QUESTION # 416
You are developing a machine learning, experiment by using Azure. The following images show the input and output of a machine learning experiment:

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 417
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2- based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 418
You manage an Azure Machine Learning workspace. You configure an automated machine learning regression training job by using the Azure Machine Learning Python SDK v2. You configure the regression job by using the following script:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer:

Explanation:

Explanation:


NEW QUESTION # 419
You are using an Azure Machine Learning workspace. You set up an environment for model testing and an environment for production.
The compute target for testing must minimize cost and deployment efforts. The compute target for production must provide fast response time, autoscaling of the deployed service, and support real-time inferencing.
You need to configure compute targets for model testing and production.
Which compute targets should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: Local web service
The Local web service compute target is used for testing/debugging. Use it for limited testing and troubleshooting. Hardware acceleration depends on use of libraries in the local system.
Box 2: Azure Kubernetes Service (AKS)
Azure Kubernetes Service (AKS) is used for Real-time inference.
Recommended for production workloads.
Use it for high-scale production deployments. Provides fast response time and autoscaling of the deployed service Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target


NEW QUESTION # 420
You manage an Azure AI Foundry project in your subscription. You deploy a gpt-4o model. You must test the model before you use it in an existing front-end application. You need to adjust the parameters to get more creative responses.
Solution: Increase Temperature.
Does the solution meet the goal?

Answer: A


NEW QUESTION # 421
......

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